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1,923 results for “Compound”
Text-fig. 7. Sapindaceae, Polygalaceae, Malvaceae. a: cf. Dipteronia or Koelreuteria pinnately compound leaf with serrate leaflets, UAPC-ALTA S 59500. b, c: Dipteronia brownii, UAPC-ALTA S 59494AB, UAPC-ALTA S 59488. d: Koelreuteria dilcheri fruit valve, BBM-PAL-P000011. e: Acer, BBM-PAL-P000012. f: Detail of Deviacer fruit body from (h). g: Florissantia quilchenensis calyx, UAPC-ALTA S 26364. h: Deviacer wolfei. UAPC-ALTA S 67690. i, j: Florissantia quilchenensis calyces. i: BBM-PAL-P000013. j: BBM-PAL-P000014. Scale bars: a–e, g–j = 1 cm; f = 5 mm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 7. Sapindaceae, Polygalaceae, Malvaceae. a: cf. Dipteronia or Koelreuteria pinnately compound leaf with serrate leaflets, UAPC-ALTA S 59500. b, c: Dipteronia brownii, UAPC-ALTA S 59494AB, UAPC-ALTA S 59488. d: Koelreuteria dilcheri fruit valve, BBM-PAL-P000011. e: Acer, BBM-PAL-P000012. f: Detail of Deviacer fruit body from (h). g: Florissantia quilchenensis calyx, UAPC-ALTA S 26364. h: Deviacer wolfei. UAPC-ALTA S 67690. i, j: Florissantia quilchenensis calyces. i: BBM-PAL-P000013. j: BBM-PAL-P000014. Scale bars: a–e, g–j = 1 cm; f = 5 mm.
Cell Painting Dataset of EUbOPEN compounds
<p>This parquet file is a dataset of cell painting profiles generated using a technique of morphological profiling known as the Cell Painting Assay (CPA). The CPA aims at deeply characterizing modulator from their ‘fingerprint’ in human cells as seen in microscopic images.</p> <p>Cells subjected to treatments are fixed, stained and imaged. These images are analysed and more than 7500 features are extracted using a cell profiler pipeline. Single cell level profiles are aggregated into well-level profiles which can be used to compare and contrast chemical treatments. </p> <p>Morphological profiling using cell painting begins with the culture of bone sarcoma U2OS cells in control and perturbation conditions for 24 to 48 hours. These cells are then subjected to 4% PFA fixation and staining with 6 inorganic dyes (Hoechst 33342 Nuclear Stain, Concanavalin A 488, Nucleic Acid Stain, WGA 555, Phalloidin 568, Mitotracker Mitochondrial stain 641) in order to mark 8 cellular components (nucleus, endoplasmic reticulum, mitochondria, actin, Golgi, plasma membrane, nucleolus and RNA). Images at 20x magnification are then captured using a high content microscope Yokogawa Cell Voyager CV8000. These images go through a rigorous quality control process are then passed through the profiling pipeline using the open-source software Cell Profiler. Thousands of features are extracted from each image and value of each feature is computed so as to detect even the slightest phenotypic variation occurring in cells in an extremely sensitive manner.</p> <p>This dataset consists of profiles of 595 compounds from the EUbOPEN consortium (cell painting first batch) in 106 plates in the 384 well format. DMSO and positive controls are present in columns 1,2, 23 and 24. There are 39206 rows, each row corresponding to one well, and 7721 columns. The treatments have been done in a range of 8 doses and the highest dose treatment has been done at 2 time points. </p> <p> </p>
Figure 7 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 7. Results of behavioral experiments and ERG measurements. A. Behavioral experimental equipment (DA—Dark area; SA— Standing area; LA—Light area). B. Daily activities of normal form Callosobruchus maculatus. C. Quantification of ERG voltage responses of the flight and normal form insects exposed to different light stimuli. Different letters indicate significant differences between ERG responses. D. The phototaxis responses of the flight (above) and normal (down) forms were classified into 'positive phototaxis', 'negative phototaxis', and 'no selection'. E. Comparison of the phototaxis responses of the normal form and flight form Callosobruchus maculatus in response to different colors of light. Data are presented as mean ± standard error of the mean, **p <0.01, ***p <0.001 (t tests).
Figure 4 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 4. Three-dimensional reconstruction of the compound eye of the flight form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.
Figure 1 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 1. External appearance of compound eyes of Callosobruchus maculatus obtained via SEM. A–D. Laterial view of head. E–H. Vertical view of head. A, E. Flight form female (FF). B, F. Flight form male (FM). C, G. Normal form female (NF). D, H. Normal form male (NM). Abbreviations: AS—antennal socket; CE—compound eye. Scale bars = 100 μm.
Figure 6 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 6. Ultrastructure of compound eye of Callosobruchus maculatus. A, D. Longitudinal section of the cornea. B, E. Cross-section of the distal end of the rhabdom. C, F. Cross section of proximal end of the rhabdom. G, H. Longitudinal section of compound eye. I. Semischematic drawing of one ommatidium of Callosobruchus maculatus. A, B, C, G. Normal form male. D, E, F, H. Flight form male. Abbreviations: Co—cornea; CC—crystalline cone; PPC—primary pigment cell; SPC—secondary pigment cell; Rh—rhabdom; R1–R8—retinular cells; Rh7, Rh8—rhabdomere; PG—pigment granule; CCN—nuclei of cone cells; PCN—nuclei of primary pigment cells; RCN—nuclei of retinular cells. Scale bars: A–B, D–E = 5 μm; C, F = 0.5 μm; G–H = 10 μm.
Figure S3 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure S3. Electrophysiological waveforms of compound eyes of two types of Callosobruchus maculatus. A. White. B. Green (520–530 nm). C. Blue (460–470 nm). D. Ultraviolet (365 nm). E. Red (620–630 nm).
Figure 5 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 5. Three-dimensional reconstruction of the compound eye of the normal form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.
Figure S2 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure S2. The projection of microCT of Callosobruchus maculatus. A. Flight form. B. Normal form. Abbreviations: S—baseline length of a segment; H—height.
Figure 3 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 3. Differences in the areas and numbers of ommatidia observed in two types of Callosobruchus maculatus. A. The ommatidia areas of the flight and normal forms. B. The number of ommatidia compared between two types of Callosobruchus maculatus. *p <0.05; **p <0.01; n.s., indicates no significant difference (t tests).
Figure 2 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 2. Partially external appearance of compound eyes of Callosobruchus maculatus obtained via SEM. A–D. Hexagonal ommatidia of compound eye (H). E–H. Pentagonal and irregular ommatidia of compound eye (P). I–L. The arrows point to the interfacetal hairs between the hexagonal ommatidia. M–P. The arrows point to the interfacetal hairs between the pentagonal and irregular ommatidia. A, E, I, M. Flight-form female (FF). B, F, J, N. Flight form male (FM). C, G, K, O. Normal form female (NF). D, H, L, P. Normal form male (NM). Scale bars = 10 μm.
Metabolomics WorkBench Compound Dictionary
<p>mwTAB file for each study in the Metabolomics Workbench (https://www.metabolomicsworkbench.org/) was processed through an automated data merging pipeline. It yielded -</p> <p> </p> <ul> <li>~237,333 unique chemical names</li> <li>~13,810 PubChem CIDs</li> <li>~14,298 KEGG IDs</li> <li>~5,130 CAS Numbers</li> <li>- 147 unique species</li> <li>- 175 unique sample type</li> </ul> <p> </p> <p>Note: workbench_curated_compound_list.csv contains some of the curated compound names. Basic curation has happened to connect metabolites names to PubChem identifiers. </p> <p> </p>
Morphing libraries, QSAR models, and compounds predicted to be active on the Glucocorticoid receptor (GR)
<p>This repository contains datasets and files related to the computational drug discovery project of the chemical space exploration of the Glucocorticoid receptor. The accompanying Python code is freely available in the GitHub repository (<a title="https://github.com/Iagea/GRML_analyses" href="https://github.com/Iagea/GRML_analyses" target="_blank" rel="noreferrer noopener">https://github.com/Iagea/GRML_analyses</a>).</p> <p><strong>Morphing Libraries:</strong></p> <ul> <li><strong>GRML_library.csv:</strong> The GRML library is the collection of 999,015 virtual compounds generated by Molpher [1-2] starting from GR ligands with unique Bemis-Murcko scaffolds collected from the ChEMBL17 and IMG libraries.</li> <li><strong>RML_library.csv:</strong> The RML library is the collection of 1,346,310 virtual compounds generated by Molpher starting from compounds with unique Bemis-Murcko scaffolds randomly selected from the ZINC database.</li> </ul> <p><strong>IMG library:</strong></p> <ul> <li><strong>IMG_non_proprietary.csv</strong>: The non-proprietary IMG library subset containing 12,956 compounds and their corresponding B-scores from the primary screen.</li> </ul> <p><strong>Molpher inputs:</strong></p> <ul> <li><strong>GR_inputs.csv</strong>: The GR inputs are the ligands used to create the GRML library, 204 compounds from ChEMBL17 (95 compounds) and the non-proprietary dataset from IMG (109 compounds).</li> <li><strong>Random_inputs.csv</strong>: The random inputs are 249 random ZINC compounds used to create the Random library.</li> </ul> <p><strong>Model's training sets:</strong></p> <ul> <li><strong>Model33_training_set.csv</strong>: Random forest classification model training set, it includes 865 compounds; known GR actives and inactives from ChEMBL33 (738 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>Model17_training_set.csv</strong>: Random forest classification model training set, it includes 601 compounds; known GR actives and inactives from ChEMBL17 (474 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>RFR_training_set.csv</strong>: Random forest regression model training set, it includes 89 compounds; known GR actives and inactives from ChEMBL33 that fit into the GR pharmacophore with the four features we describe in our paper.</li> </ul> <p><strong>Models:</strong></p> <ul> <li><strong>Model33.pkl:</strong> Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL33 and IMG libraries.</li> <li><strong>Model17.pkl</strong>: Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL17 and IMG libraries.</li> <li><strong>RFR_models.pkl</strong><em>: </em>Python pickle file containing the 100 trained random forest regression models used to rank the proposed active morphs. These models were trained with the RFR_training_set.csv.</li> </ul> <p><strong>Active predicted morphs:</strong></p> <ul> <li><strong>all_morphs_actives</strong><em><strong>_</strong></em><strong>predicted.xlsx:</strong> An Excel spreadsheet containing two sheets. 1) All 22,524 GRML active predicted morphs. 2) All 4,341 RML active predicted morphs. The QED, NIBR Severity Score, and Molskill Score are given for each morph.</li> </ul> <p><strong>Proposed GR active ligands:</strong></p> <ul> <li><strong>designed_ligands.xlsx</strong>: An Excel spreadsheet containing two sheets. 1) All 54 designed GR ligands with their QED, NIBR severity score, MolSkill score, consensus ranking from the 100 RFR models, and the result of the manual annotation and remarks, if available. 2) The structure of the 54 ligands based on their manual annotation and presence or not in ChEMBL33 database.</li> </ul> <p>Researchers and professionals in the field of drug discovery and cheminformatics may find these resources useful for further analysis and investigations.</p> <p><strong>Bibliography</strong></p> <p>[1] Hoksza, D., Škoda, P., Voršilák, M. <em>et al.</em> Molpher: a software framework for systematic chemical space exploration. <em>J Cheminform</em> <strong>6</strong>, 7 (2014). https://doi.org/10.1186/1758-2946-6-7</p> <p>[2] <a href="https://github.com/lich-uct/molpher-lib">https://github.com/lich-uct/molpher-lib</a></p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
Global LAke Surface water Temperature (GLAST): Compound thermal extremes in lakes
<p>This database contains daily maximum temperature, daily minimum temperature, and daily mean temperature for 92,245 lakes globally from 1981 to 2020. These daily time series were derived from hourly simulation data.</p> <p>The database also includes annual statistics of six types of thermal extreme events calculated from the daily lake temperature time series:</p> <ul> <li>daytime hot extreme events (hot day–mild night)</li> <li>nighttime hot extreme events (mild day–hot night)</li> <li>compound hot extreme events (hot day–hot night)</li> <li>daytime cold extreme events (cold day–mild night)</li> <li>nighttime cold extreme events (mild day–cold night)</li> <li>compound cold extreme events (cold day–cold night)</li> </ul> <p> </p> <p>The annual statistics provided for these events include metrics such as frequency, intensity, duration, and total days. Additionally, annual statistics of extreme air temperature events over the lakes are included.</p> <p>Details about the hourly-scale lake temperature simulation methodology can be found in the paper <em>"Global lakes are warming slower than surface air temperature due to accelerated evaporation"</em> (Tong et al., 2023, Nature Water). Definitions and calculation methods for thermal extreme events in lakes and atmosphere are provided in <em>"Day-night compound thermal extremes in lakes"</em> (Tong et al., 2025).</p> <p>For detailed information about the contents of each data file, please refer to the accompanying <strong>readme.docx</strong> file.</p> <p>For more datasets on global aquatic environments, please visit the official website of the Global Aqua Remote Sensing (GARS) Laboratory, led by Prof. Lian Feng: <a href="https://garslab.com/?cat=1">https://garslab.com/?cat=1</a>.</p>
Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes
<p>Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes</p> <p>DOI: 10.1039/d3tc03693f</p> <p>V. García-López, F. Marques-Moros, J. Troya, J. Canet-Ferrer, M. Clemente. León, E. Coronado</p> <p>J. Mater. Chem. C, 12, 161-169 (2024)</p>
A phenopushing platform to identify compounds that alleviate acute hypoxic stress by fast-tracking cellular adaptation
<p><span>Severe acute hypoxic stress is a major contributor to the pathology of human diseases, including ischemic disorders. Current treatments focus on managing consequences of hypoxia, with few addressing cellular adaptation to low-oxygen environments. Here, we investigate whether accelerating hypoxia adaptation could provide a strategy to alleviate acute hypoxic stress. We develop a high-content phenotypic screening platform to identify compounds that fast-track adaptation to hypoxic stress. </span><span>Our platform</span><span> captures a high-dimensional phenotypic hypoxia response trajectory consisting of normoxic, acutely stressed, and chronically adapted cell states. Leveraging this trajectory, we identify compounds that phenotypically shift cells from the acutely stressed towards the adapted state, revealing mTOR/PI3K or BET inhibition as strategies to induce this phenotypic shift. Importantly, </span><span>our</span><span> compound hits promote the survival of liver cells exposed to ischemia-like stress, and rescue cardiomyocytes from hypoxic stress. Our “phenopushing” platform offers a general, target-agnostic approach to identify compounds and targets that accelerate cellular adaptation, applicable across various stress conditions.</span></p>
Highly curated hERG dataset of 8879 unique molecular compounds with corresponding potency values
<p>This dataset was built during a research project, in the field of Computer-Aided Drug Discovery (CADD), funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. The aim of the project was to build descriptor-based machine learning models for hERG cardiotoxicity liability predictions. The dataset includes a total of 8879 unique molecular compounds gathered from ChEMBL and PubChem publicly available bioactivity databases, as well as from literature mining. The list is split into 2 sets, 8380 for training and 499 for testing. All molecular compounds are represented in their SMILE format with their corresponding PIC50 potency values.</p> <p>To access the full original work, please visit the following link: <a href="https://arxiv.org/abs/2112.13467">Manuscript</a></p> <p><strong>Note:</strong> Upon usage of this data, kindly cite the original manuscript describing the curation process:<br><br>Arab, Issar, and Khaled Barakat. "ToxTree: descriptor-based machine learning models for both hERG and Nav1. 5 cardiotoxicity liability predictions." <em>arXiv preprint arXiv:2112.13467</em> (2021).</p> <p> </p> <p><strong>Refer to our latest manually curated and a much larger dataset here: <a href="../record/8359714">link</a> </strong></p>
Highly curated Nav1.5 dataset of 1723 unique molecular compounds with corresponding potency values
<p>This dataset was built during a research project, in the field of Computer-Aided Drug Discovery (CADD), funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. The aim of the project was to build descriptor-based machine learning models for Nav1.5 cardiotoxicity liability predictions. The dataset includes a total of 1723 unique molecular compounds gathered from ChEMBL and PubChem publicly available bioactivity databases. The list is split into 2 sets, 1550 for training and 173 for testing. All molecular compounds are represented in their SMILE format with their corresponding PIC50 potency values.</p> <p>To access the full original work, please visit the following link: <a href="https://arxiv.org/abs/2112.13467">Manuscript</a> <br><strong>Refer to a much larger and latest dataset here: <a href="../record/8359714">link</a> </strong></p> <p><br><strong>Note:</strong> Upon usage of this data, kindly cite both the dataset and the original manuscript describing the curation process as written below.<br><br><em>Arab I, Barakat K. ToxTree: Descriptor-based machine learning models for both hERG and Nav1. 5 cardiotoxicity liability predictions. arXiv preprint arXiv:2112.13467. 2021</em></p> <p><em>Arab, Issar, & Barakat, Khaled. (2021). Highly curated Nav1.5 dataset of 1723 unique molecular compounds with corresponding potency values [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5807731</em></p>
Candidate compounds from the design of covalent Bruton's tyrosine kinase (BTK) inhibitors via focused deep generative modeling
<pre>A total of 1491 candidate inhibitors for covalent inhibition of BTK are deposited as SMILES strings together with 34 known covalent BTK inhibitors used to guide generative computational design. The study will be reported in a publication in Molecules under the authors' names.</pre>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.